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Erschienen in: Memetic Computing 1/2019

29.06.2017 | Regular Research Paper

An improved weighted extreme learning machine for imbalanced data classification

verfasst von: Chengbo Lu, Haifeng Ke, Gaoyan Zhang, Ying Mei, Huihui Xu

Erschienen in: Memetic Computing | Ausgabe 1/2019

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Abstract

This paper proposes an improved weighted extreme learning machine (IW-ELM) for imbalanced data classification. By incorporating voting method into weighted extreme learning machine (weighted ELM), three major steps are involved in the proposed method: training weighted ELM classifiers, eliminating unusable classifies to determine proper classifiers for voting, and finally determining the classification result based on majority voting. Simulations on many real world imbalanced datasets with various imbalance ratios have demonstrated that the proposed algorithm outperforms weighted ELM and other related classification algorithms.

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Metadaten
Titel
An improved weighted extreme learning machine for imbalanced data classification
verfasst von
Chengbo Lu
Haifeng Ke
Gaoyan Zhang
Ying Mei
Huihui Xu
Publikationsdatum
29.06.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 1/2019
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-017-0236-3

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